Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is proposed.The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature information.At the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image information.This study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional algorithms.The methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other algorithms.The algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)index.Comprehensive experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.

Infrared polarization image fusion based on combination of NSST and improved PCA

In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.

Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared(IR)/visible(VS)images.Dissimilarities in various kind of features in these images are vital to preserve in the single fused image.Hence,simultaneous preservation of both the aspects at the same time is a challenging task.However,most of the existing methods utilize the manual extraction of features;and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image.Therefore,this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.Firstly,fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the image.Secondly,images have been learned by two parallel branches of the siamese convolutional neural network(CNN)to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image information.Finally,the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixelwise strategy to result in fused image.Different parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information(MI),0.841 for entropy(EG),0.655 for edge information(EI),0.652 for human perception(HP),and 0.980 for image structural similarity(ISS).Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform(DCT),anisotropic diffusion&karhunen-loeve(ADKL),guided filter(GF),random walk(RW),principal component analysis(PCA),and convolutional neural network(CNN)methods.

An infrared and visible image fusion method based upon multi-scale and top-hat transforms

The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.

Sub-Regional Infrared-Visible Image Fusion Using Multi-Scale Transformation

Infrared-visible image fusion plays an important role in multi-source data fusion,which has the advantage of integrating useful information from multi-source sensors.However,there are still challenges in target enhancement and visual improvement.To deal with these problems,a sub-regional infrared-visible image fusion method(SRF)is proposed.First,morphology and threshold segmentation is applied to extract targets interested in infrared images.Second,the infrared back-ground is reconstructed based on extracted targets and the visible image.Finally,target and back-ground regions are fused using a multi-scale transform.Experimental results are obtained using public data for comparison and evaluation,which demonstrate that the proposed SRF has poten-tial benefits over other methods.

Automated Registration for Infrared Image Based on Wavelet Analysis

To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.

Multiscale feature learning and attention mechanism for infrared and visible image fusion

Current fusion methods for infrared and visible images tend to extract features at a single scale,which results in insufficient detail and incomplete feature preservation.To address these issues,we propose an infrared and visible image fusion network based on a multiscale feature learning and attention mechanism(MsAFusion).A multiscale dilation convolution framework is employed to capture image features across various scales and broaden the perceptual scope.Furthermore,an attention network is introduced to enhance the focus on salient targets in infrared images and detailed textures in visible images.To compensate for information loss during convolution,jump connections are utilized during the image reconstruction phase.The fusion process utilizes a combined loss function consisting of pixel loss and gradient loss for unsupervised fusion of infrared and visible images.Extensive experiments on the dataset of electricity facilities demonstrate that our proposed method outperforms nine state-of-theart methods in terms of visual perception and four objective evaluation metrics.

Multi-sensors Image Fusion via NSCT and GoogLeNet

In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.

Vision Enhancement Technology of Drivers Based on Image Fusion

The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.

Research on Face Anti-Spoofing Algorithm Based on Image Fusion

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.

Fusion of Infrared and Visible Light Images Based on Region Segmentation

This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...

面向双模态夜视图像的混合尺度融合算法

针对传统红外与可见光图像融合算法存在的细节模糊、对比度降低、背景信息缺失等不足,提出了一种基于混合尺度的红外与可见光融合方法。通过潜在低秩表示变换将源图像分解低秩子带和显著子带;利用非下采样轮廓波变换将低秩子带继续分解为低频分量与高频分量;针对显著子带采用基于卷积稀疏表示的方法进行融合;并结合全局均值、区域均值与能量的优势融合低频分量;利用权重决策图融合高频分量。基于自建库及公开库的实验结果表明,与其他5种图像融合算法相比,所提算法在充分继承源图像有效信息的同时,融合图像整体对比度更均衡,有效提升了融合图像的清晰度,包含更丰富的图像细节信息,在主客观评价上均取得了更好的效果。

基于目标增强与鼠群优化的红外与可见光图像融合算法

针对传统红外与可见光图像融合结果存在目标模糊、信息丢失问题,提出一种基于目标增强与鼠群优化的红外与可见光图像融合方法,记为TERSFuse。为了减少融合结果中原始图像细节信息丢失,分别构建了红外对比度增强模块和基于亮度感知的可见光图像增强模块;利用拉普拉斯金字塔变换对红外和可见光增强图像进行多尺度分解,从而得到对应的高、低频图像;为了使融合结果充分保留原始图像信息,分别采用“最大绝对值”规则对红外和可见光高频图像进行融合以及通过计算权重系数对低频图像进行融合;设计了基于鼠群优化的图像重构模块以实现高频图像和低频图像重构权重的自适应分配,进而提高融合图像的视觉效果。为了验证所提算法优势,与7种经典融合算法进行比较,实验结果表明所提算法不仅具有良好的视觉效果,而且融合图像能够保留原始图像丰富的边缘纹理和对比度信息。

基于自编码器的红外与可见光图像融合算法

针对目前红外与可见光图像融合过程中,图像特征提取不充分、中间层信息丢失以及融合图像细节不够清晰的问题,提出了一种基于自编码器的端到端图像融合网络结构。该网络由编码器、融合网络和解码器3部分组成。将高效通道注意力机制和混合注意力机制引入到编码器和融合网络中,利用卷积残差网络(convolutional residual network,CRN)基本块来提取并融合红外图像和可见光图像的基本特征,然后将融合后的特征图输入到解码器进行解码,重建出融合图像。选取目前具有典型代表性的5种方法在主客观方面进行对比。在客观方面,较第2名平均梯度、空间频率和视觉保真度分别提升了21%、10.2%、7.2%。在主观方面,融合后的图像目标清晰、细节突出、轮廓明显,符合人类视觉感受。

利用Transformer的多模态目标跟踪算法

目前目标跟踪方法大多通过融合不同模态信息进行定位决策,存在信息提取不充分、融合方法简单、弱光场景无法准确跟踪目标的问题。为此,提出一种基于Transformer的多模态目标跟踪算法(Trans-RGBT):利用伪孪生网络对可见光图像和红外图像分别进行特征提取,并在特征层面充分融合;将首帧目标信息调制到待跟踪帧的特征向量中,得到一个专用跟踪器;应用Transformer的方法对视野中的目标进行编解码,通过空间位置预测分支预测目标在视野中的空间位置,并结合历史信息滤除干扰目标,得到目标的准确位置;使用矩形框回归网络预测目标的外接矩形框,从而实现目标准确跟踪。在最新的大规模数据集VTUAV、RGBT234上进行了实验,与孪生网络(Siambased)、滤波(filter-based)算法相比,Trans-RGBT精度更高、鲁棒性更好、速度接近实时,达22 FPS。

基于特征相似性的红外与可见光图像融合方法

单一图像无法全面描述目标的信息,实际应用价值低,针对当前红外与可见光图像融合方法存在的一些不足,如:融合质量差等,为了获得更加理想的红外与可见光图像融合效果,提出了基于特征相似性的红外与可见光图像融合方法。首先分析当前红外与可见光图像融合的研究进展,指出各种方法的局限性,然后采用红外图像和可见光图像,并对它们进行图像去噪、增强处理,采用卷积神经网络提取红外与可见光图像的特征,最后根据特征相似性进行红外与可见光图像融合,并对红外与可见光图像融合效果进行了测试,结果表明,本方法提升了红外与可见光图像融合质量,融合效果要明显优于其他红外与可见光图像融合方法。